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Title: Modeling respiratory illnesses with change point : a lesson from the SARS epidemic in Hong Kong
Authors: Wong, H 
Shao, Q
Ip, WC
Keywords: Air pollution
Change point problem
Multiple index model
Non-parametric regression
Public health
Respiratory illness
Severe acute respiratory syndrome (SARS)
Issue Date: 2013
Publisher: Elsevier
Source: Computational statistics and data analysis, 2013, v. 57, no. 1, p. 589-599 How to cite?
Journal: Computational Statistics and Data Analysis 
Abstract: It is generally agreed that respiratory disease is closely related to ambient air quality and weather conditions. Besides, hygiene related factors such as the public health measures by the government and possible personal awareness in the community can also affect the spread of infectious respiratory diseases. However, there is no quantitative support for this conclusion, because of lack of quality data. The severe acute respiratory syndrome (or SARS) outbreak in 2003 triggered strict public health measures and personal awareness in the prevention of infectious respiratory diseases, providing us an opportunity to quantify the impact of hygiene related factors in the spread of the disease. In this paper, we model the number of the respiratory illnesses by a semiparametric model which models the environmental and weather impacts using a multiple index model and the impact of other public health measures and possible personal awareness using a growth curve with jump. Using data from Hong Kong, we found that public health measures contributed to about 39% of reduction in the number of respiratory illnesses during the SARS period. However, the impact of hygienically related factors eventually fades as time passes. The results provide indirect quantitative support to the usefulness of governmental campaigns to arouse the awareness of the public in staying away from transmission of respiratory diseases during the full outbreak of the disease. The results also show the fast fading of alertness of Hong Kong people towards the epidemic. Furthermore, our model also offers a way to model the impacts of environmental factors on respiratory diseases, when the data contains the effect of human intervention, by introducing the change point and growth curve to remove such an effect.
ISSN: 0167-9473
DOI: 10.1016/j.csda.2012.07.029
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